Location Integration and Data Markets

2019 ◽  
pp. 66-88
Author(s):  
Rowan Wilken
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Case Studies ◽  
Data Capture ◽  
Social Networking Service ◽  
Social Media Data ◽  
Locative Media ◽  
Mobile Social Networking ◽  
Revenue Models ◽  
Media Data

This chapter explores the still-evolving business and revenue models and geolocation data capture efforts of two commercial businesses now central to the contemporary settlement of locative media: Foursquare and Facebook. In Foursquare’s case, it underwent a quite dramatic series of transformations, evolving from a check-in based mobile social networking service, to a search and recommendation service, and now also serving as a firm offering location intelligence related enterprise services. In Facebook’s case, it set about further strengthening its grip on social media data markets by adding geolocation functionalities and geodata capture capabilities to its social networking operations. These two case studies provide a rich composite picture of the business ecologies of locational information. The aim in selecting these cases is to develop a clearer understanding of how both firms accrue location data and how they extract location value—that is, how this information is shared, harvested, valued, reused, and commodified.

scholarly journals Using Social Media Data as Research Data

2013 ◽  
Vol 1 (3) ◽  
pp. 49-55
Author(s):  
Suman Silwal ◽  
Dale W Callahan
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Communication Channel ◽  
Social Networking Site ◽  
Science And Engineering ◽  
Social Media Data ◽  
Breaking News ◽  
Current State ◽  
Media Data

Social Media (SM) is becoming a normal part of everyday life. The information generated from Social Media (SM) data is becoming increasingly utilized as a communication channel for market trend, brand awareness, breaking news, and online social interaction between person to person. SM is also rapidly growing and maturing [1]. Further, SM is becoming a reliable tool for interdisciplinary industries like banks, travel, healthcare, biotech, software, sports etc.SM data can also be used as a research tool to apply in different areas of Humanities, Art, Science and Engineering. There are unlimited possibilities using Social Networking Site (SNS) to collect, process and evaluate data. This paper reviews the current state of Social Networking Sites and Text-based Language Processes, and how it can be used to generate valuable information.

scholarly journals Detect Extreme Sentiments on Social Networks using BERT

2021 ◽  
Author(s):  
Muhammad Luqman Jamil ◽  
Sebastião Pais ◽  
João Cordeiro ◽  
Gaël Dias
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Public Opinion ◽  
Social Network ◽  
Social Networking ◽  
Online Social Networking ◽  
Social Media Data ◽  
Preceding Work ◽  
Unsupervised Approach ◽  
Media Data

Abstract Online social networking platforms allow people to freely express their ideas, opinions, and emotions negatively or positively. Previous studies have examined user’s sentiments on these platforms to study their behaviour in different contexts and purposes. The mechanism of collecting public opinion information has attracted researchers to automatically classify the polarity of public opinions based on the use of concise language in messages, such as tweets, by analyzing social media data. In this paper, we extend the preceding work [1], by proposing an unsupervised approach to automatically detect extreme opinions/posts in social networks. We have evaluated our performance on five different social network and media datasets. In this work, we use the semi-supervised approach BERT to check the accuracy of our classified dataset. The latter task shows that, in these datasets, posts that were previously classified as negative or positive are, in fact, extremely negative or positive in many cases.

Social media data

2019 ◽  
pp. 47-60
Author(s):  
Adrian Tear ◽  
Humphrey Southall
Keyword(s):  
Social Media ◽  
Population Based ◽  
Computational Social Science ◽  
Data Capture ◽  
Personal Privacy ◽  
User Privacy ◽  
Social Media Data ◽  
Data Validity ◽  
Media Data ◽  
By Products

The increasing availability of huge volumes of social media ‘Big Data’ from Facebook, Flickr, Instagram, Twitter and other social network platforms, combined with the development of software designed to operate at web scale, has fuelled the growth of computational social science. Often analysed by ‘data scientists’, social media data differ substantially from the datasets officially disseminated as by-products of government-sponsored activity, such as population censuses or administrative data, which have long been analysed by professional statisticians. This chapter outlines the characteristics of social media data and identifies key data sources and methods of data capture, introducing several of the technologies used to acquire, store, query, visualise and augment social media data. Unrepresentativeness of, and lack of (geo)demographic control in, social media data are problematic for population-based research. These limitations, alongside wider epistemological and ethical concerns surrounding data validity, inadvertent co-option into research and protection of user privacy, suggest that caution should be exercised when analysing social media datasets. While care must be taken to respect personal privacy and sample assiduously, this chapter concludes that statisticians, who may be unfamiliar with some of the programmatic steps involved in accessing social media data, must play a pivotal role in analysing it.

Measuring Public Opinion with Social Media Data

2017 ◽  
Author(s):  
Marko Klašnja ◽  
Pablo Barberá ◽  
Nick Beauchamp ◽  
Jonathan Nagler ◽  
Joshua A. Tucker
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Social Networking ◽  
Social Networking Sites ◽  
Research Agenda ◽  
The Novel ◽  
Social Media Data ◽  
Public Opinion Surveys ◽  
New Directions ◽  
Media Data

This chapter examines the use of social networking sites such as Twitter in measuring public opinion. It first considers the opportunities and challenges that are involved in conducting public opinion surveys using social media data. Three challenges are discussed: identifying political opinion, representativeness of social media users, and aggregating from individual responses to public opinion. The chapter outlines some of the strategies for overcoming these challenges and proceeds by highlighting some of the novel uses for social media that have fewer direct analogs in traditional survey work. Finally, it suggests new directions for a research agenda in using social media for public opinion work.

scholarly journals Informed consent for linking survey and social media data - Differences between platforms and data types

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Johannes Breuer ◽  
Tarek Al Baghal ◽  
Luke Sloan ◽  
Libby Bishop ◽  
Dimitra Kondyli ◽  
...  
Keyword(s):  
Social Media ◽  
Informed Consent ◽  
Data Collection ◽  
Case Studies ◽  
Survey Data ◽  
Data Types ◽  
Social Media Data ◽  
Key Aspects ◽  
Media Data ◽  
Collection Methods

Linking social media data with survey data is a way to combine the unique strengths and address some of the respective limitations of these two data types. As such linked data can be quite disclosive and potentially sensitive, it is important that researchers obtain informed consent from the individuals whose data are being linked. When formulating appropriate informed consent, there are several things that researchers need to take into account. Besides legal and ethical questions, key aspects to consider are the differences between platforms and data types. Depending on what type of social media data is collected, how the data are collected, and from which platform(s), different points need to be addressed in the informed consent. In this paper, we present three case studies in which survey data were linked with data from 1) Twitter, 2) Facebook, and 3) LinkedIn and discuss how the specific features of the platforms and data collection methods were covered in the informed consent. We compare the key attributes of these platforms that are relevant for the formulation of informed consent and also discuss scenarios of social media data collection and linking in which obtaining informed consent is not necessary. By presenting the specific case studies as well as general considerations, this paper is meant to provide guidance on informed consent for linked survey and social media data for both researchers and archivists working with this type of data.

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